Year: 2024
Type: article
Abstract: A comparison between four Genetic Programming techniques is presented in this paper. The compared methods are Multi-Expression Programming, Gene Expression Programming, Grammatical Evolution, and Linear Genetic Programming. The comparison includes all aspects of the considered evolutionary algorithms: individual representation, fitness assignment, genetic operators, and evolutionary scheme. Several numerical experiments using five benchmarking problems are carried out. Two test problems are taken from PROBEN1 and contain real-world data. The results reveal that Multi-Expression Programming has the best overall behavior for the considered test problems, closely followed by Linear Genetic Programming.
Source: Complex Systems
Authors: Electronic mail address: [email protected]., Mihai Oltean, Crina Groşan
Institution: Babeș-Bolyai University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2169840877